Combining Genomics, Remote Sensing, and Geospatial Modeling to Understand Adaptation to Growing Season Length in Balsam Poplar

结合基因组学、遥感和地理空间建​​模来了解香脂杨对生长季节长度的适应

• 批准号: 1238885
• 负责人: Stephen Keller
• 金额: $ 149.57万
• 依托单位: University of Maryland Center for Environmental Sciences
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-01-15 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1238885&HistoricalAwards=false
• 关键词: Combining; Genomics; Remote; Sensing; Geospatial

PI: Stephen Keller (University of Maryland Center for Environmental Science, Frostburg, MD)CoPIs: Andrew Elmore, Matthew Fitzpatrick, David Nelson, and Cathlyn Stylinski (University of Maryland Center for Environmental Science, Frostburg, MD)Key Collaborator: Raju Soolanayakanahally (Agriculture and Agri-Food, Canada)Translating genomic information into knowledge of environmental adaptation and prediction of performance under field conditions are core challenges facing plant biologists. The goal of associating genome-wide diversity to functional plant phenotypes has created emerging needs for high-throughput phenotyping under field conditions and new analytical tools that can identify and visualize the relationships between genomic diversity and the environment. This project will integrate tools from genomics, remote sensing, and geospatial modeling to study the genetic basis of climate adaptation in balsam poplar, Populus balsamifera, a keystone tree species in North America. Sampling will be focused on balsam poplar's southern range edge in order to study the physiological adaptations of populations to the warmest, earliest onset growing seasons within its geographic range. Genome-wide single nucleotide polymorphism (SNP) data will be generated for 600 poplar genotypes and used to perform genome scans for local adaptation and association mapping for phenology, growth, and water use efficiency traits. Regions of the genome associated with climate adaptation will be used to predict field performance using an independent sample of genotypes and an innovative remote sensing approach to measure phenology. New spatial analytical methods will be developed to characterize the associations between genomic variation and environmental gradients of climate and growing season length, and to visualize the landscape surface of adaptive variation under both current and projected climates. Cross-disciplinary training in the latest techniques in ecological genomics, remote sensing, and spatial modeling will be provided to undergraduate and graduate students. Minority and first-generation undergraduate students will be recruited through partnerships with Frostburg State University's McNair Program and other organizations. Public outreach to rural communities will be conducted through a multi-faceted science program centered on engaging the public in the science of genomics, plant phenology, and climate change, in collaboration with the National Phenology Network (www.usanpn.org/). Genomic sequence data will be publically available through NCBI's sequence read archive and DOE's Knowledgebase. SNP genotypes, phenotypic traits, and remotely sensed phenology data will be publically accessible through Data DRYAD (www.datadryad.org). A software package in landscape genomics will be developed for the R project for statistical computing, and publically accessible through the Comprehensive R Archive Network (CRAN: http://cran.r-project.org/). Finally, new germplasm and associated genomic and phenotypic results will be available upon request.

主要研究者：斯蒂芬·凯勒（马里兰州大学环境科学中心，弗罗斯特堡，马里兰州）CoPI：Andrew Elmore，Matthew菲茨帕特里克，大卫纳尔逊和Cathlyn Stylinski（马里兰州大学环境科学中心，Frostburg，MD）主要合作者：拉朱·苏拉纳亚卡纳哈利（农业和农业食品，加拿大）的将基因组信息转化为环境适应性知识和田间条件下的性能预测是植物面临的核心挑战生物学家将全基因组多样性与功能性植物表型相关联的目标已经产生了对田间条件下的高通量表型分析和新的分析工具的新兴需求，这些分析工具可以识别和可视化基因组多样性与环境之间的关系。该项目将整合基因组学，遥感和地理空间建模工具，研究北美关键树种香脂杨（Populus balsamifera）气候适应的遗传基础。采样将集中在香脂杨的南部范围边缘，以研究人口的生理适应最温暖，最早开始的生长季节在其地理范围内。全基因组单核苷酸多态性（SNP）数据将产生600杨树基因型，并用于进行基因组扫描的地方适应和关联映射的物候，生长和水分利用效率性状。与气候适应相关的基因组区域将用于预测田间表现，使用独立的基因型样本和创新的遥感方法来测量物候。将开发新的空间分析方法，以表征基因组变异与气候和生长季节长度的环境梯度之间的关联，并将当前和预测气候下适应性变异的景观表面可视化。将为本科生和研究生提供生态基因组学、遥感和空间建模方面最新技术的跨学科培训。少数民族和第一代本科生将通过与弗罗斯特堡州立大学的麦克奈尔计划和其他组织的合作伙伴关系招募。将通过一个多方面的科学计划，与国家物候学网络（www.usanpn.org/）合作，向农村社区进行公众宣传，该计划的重点是让公众参与基因组学，植物物候学和气候变化科学。基因组序列数据将通过NCBI的序列读取档案和DOE的知识库以电子方式提供。SNP基因型、表型性状和遥感物候数据将通过Data DRYAD（www.example.com）以电子方式访问。将为R项目开发一个景观基因组学软件包，用于统计计算，可通过R综合档案网（CRAN：http：//cran.r-project.org/）检索。最后，新的种质和相关的基因组和表型结果将根据要求提供。